This paper illustrates how to calibrate five-wire resistive touch screens using neural networks. In addition, Kalman filter is applied to improve the accuracy of the estimated position.

This work develops a methodology and technique for calibration and dynamic touching position estimation of touch panels using error backpropagation neural networks (EBPNN) and Kalman filter. A neural-based calibration method is presented to determine the nonlinear mapping relationships of the measured and known touch points, and then calibrate their positions in a real-time manner. In order to obtain position estimation of fast moving points in the drawing mode, a Kalman filtering scheme is proposed to achieve a satisfactory precision.

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Kitronyx is an engineering company that provides development tools and engineering design services to individuals, companies, universities and government agencies based on expertise in force, pressure, and multi-touch technologies. Our focus is on providing the ‘closest to complete’ technology to enable customers to rapidly turn ideas into real products.

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